Large language models (LLMs) are rapidly transforming medical writing and publishing. This review article focuses on experimental evidence to provide a comprehensive overview of the current applications, challenges, and future implications of LLMs in various stages of academic research and publishing process. Global surveys reveal a high prevalence of LLM usage in scientific writing, with both potential benefits and challenges associated with its adoption. LLMs have been successfully applied in literature search, research design, writing assistance, quality assessment, citation generation, and data analysis. LLMs have also been used in peer review and publication processes, including manuscript screening, generating review comments, and identifying potential biases. To ensure the integrity and quality of scholarly work in the era of LLM-assisted research, responsible artificial intelligence (AI) use is crucial. Researchers should prioritize verifying the accuracy and reliability of AI-generated content, maintain transparency in the use of LLMs, and develop collaborative human-AI workflows. Reviewers should focus on higher-order reviewing skills and be aware of the potential use of LLMs in manuscripts. Editorial offices should develop clear policies and guidelines on AI use and foster open dialogue within the academic community. Future directions include addressing the limitations and biases of current LLMs, exploring innovative applications, and continuously updating policies and practices in response to technological advancements. Collaborative efforts among stakeholders are necessary to harness the transformative potential of LLMs while maintaining the integrity of medical writing and publishing.

The application of new technologies, such as artificial intelligence (AI), to science affects the way and methodology in which research is conducted. While the responsible use of AI brings many innovations and benefits to science and humanity, its unethical use poses a serious threat to scientific integrity and literature. Even in the absence of malicious use, the Chatbot output itself, as a software application based on AI, carries the risk of containing biases, distortions, irrelevancies, misrepresentations and plagiarism. Therefore, the use of complex AI algorithms raises concerns about bias, transparency and accountability, requiring the development of new ethical rules to protect scientific integrity. Unfortunately, the development and writing of ethical codes cannot keep up with the pace of development and implementation of technology. The main purpose of this narrative review is to inform readers, authors, reviewers and editors about new approaches to publication ethics in the era of AI. It specifically focuses on tips on how to disclose the use of AI in your manuscript, how to avoid publishing entirely AI-generated text, and current standards for retraction.

Questionable research practices (QRP) are believed to be widespread, but empirical assessments are generally restricted to a few types of practices. Furthermore, conceptual confusion is rife with use and prevalence of QRPs often being confused as the same quantity. We present the hitherto most comprehensive study examining QRPs across scholarly fields and knowledge production modes. We survey perception, use, prevalence and predictors of QRPs among 3,402 researchers in Denmark and 1,307 in the UK, USA, Croatia and Austria. Results reveal remarkably similar response patterns among Danish and international respondents (τ = 0.85). Self-reported use indicates whether respondents have used a QRP in recent publications. 9 out of 10 respondents admitted using at least one QRP. Median use is three out of nine QRP items. Self-reported prevalence reflects the frequency of use. On average, prevalence rates were roughly three times lower compared to self-reported use. Findings indicated that the perceived social acceptability of QRPs influenced self-report patterns. Results suggest that most researchers use different types of QRPs within a restricted time period. The prevalence estimates, however, do not suggest outright systematic use of specific QRPs. Perceived pressure was the strongest systemic predictor for prevalence. Conversely, more local attention to research cultures and academic age was negatively related to prevalence. Finally, the personality traits conscientiousness and, to a lesser degree, agreeableness were also inversely associated with self-reported prevalence. Findings suggest that explanations for engagement with QRPs are not only attributable to systemic factors, as hitherto suggested, but a complicated mixture of experience, systemic and individual factors, and motivated reasoning.

The significance of the ethical review process in human-based research undertakings cannot be overemphasized as it is necessary to uphold ethical standards and protect participants. However, the review process per se can act as a bottleneck, potentially hindering research progress and leading to academic dishonesty. The present work explores the benefits and challenges of ethical review, emphasizing issues like intellectual theft, forced authorship, and the stifling of independent researchers. Proposed solutions include leveraging previously approved designs, empowering experienced professors for clearance, establishing panels of researchers, creating voluntary ethical approval offices, utilizing private consultancy offices, and establishing a transnational ethical clearance authority. In conclusion, this work stresses the importance of finding mechanisms to streamline the ethical review process while maintaining ethical standards to foster integrity in research and combat academic dishonesty.

Coercion authorship (CA), typically enforced by principal investigators, has detrimental effects on graduate students, young researchers, and the entire scientific endeavor. Although CA is ubiquitous, its occurrence and major determinants have been mainly explored among graduate students and junior scientists in Sweden, Norway, and Denmark where the ratio of CA ranged from 13 to 40%. In addition to lacking comparable figures, developing countries usually lack institutional plans for promoting integrity and effective deterrents against CA and other malpractices. Hence, universities and research centers therein must publish their authorship policies and implement specific strategies to instruct graduate students, junior scientists, and experienced researchers on integrity, publishing ethics, and responsible authorship. Finally, I remark that the primary responsibility of principal researchers to promote fair authorship practices and discourage unfair ones is even greater when it comes to CA due to the asymmetrical power relationship between senior authors and novice scientists.

OBJECTIVE: The primary objective of this inquiry was to explore the nexus between authorship attribution in medical literature and accountability for scientific impropriety while assessing the influence of authorial multiplicity on the severity of sanctions imposed.
METHODS: Probit regression models were employed to scrutinize the impact of authorship on assuming accountability for scientific misconduct, and unordered multinomial logistic regression models were used to examine the influence of authorship and the number of bylines on the severity of punitive measures.
RESULTS: First authors and corresponding authors were significantly more likely to be liable for scientific misconduct than other authors and were more likely to be penalized particularly severely. Furthermore, a negative correlation was observed between the number of authors\' affiliations and the severity of punitive measures.
CONCLUSIONS: Authorship exerts a pronounced influence on the attribution of accountability in scientific research misconduct, particularly evident in the heightened risk of severe penalties confronting first and corresponding authors owing to their principal roles. Hence, scientific research institutions and journals must delineate authorship specifications meticulously, ascertain authors\' contributions judiciously, bolster initiatives aimed at fostering scientific research integrity, and uphold an environment conducive for robust scientific inquiry.

Presented here is a systematic literature review of what the academic literature asserts about: (1) the stages of the ethical decision-making process (i.e. awareness, reasoning, motivation, and action) that are claimed to be improved or not improved by RI teaching and whether these claims are supported by evidence; (2) the measurements used to determine the effectiveness of RI teaching; and (3) the stage/s of the ethical decision-making process that are difficult to assess. Regarding (1), awareness was the stage most claimed to be amenable to improvement following RI teaching, and with motivation being the stage that is rarely addressed in the academic literature. While few, some sources claimed RI teaching cannot improve specific stages. With behaviour (action) being the stage referenced most, albeit in only 9% of the total sources, for not being amenable to improvement following RI teaching. Finally, most claims were supported by empirical evidence. Regarding (2), measures most frequently used are custom in-house surveys and some validated measures. Additionally, there is much debate in the literature regarding the adequacy of current assessment measures in RI teaching, and even their absence. Such debate warrants caution when we are considering the empirical evidence supplied to support that RI teaching does or does not improve a specific stage of the decision-making process. Regarding (3), only behaviour was discussed as being difficult to assess, if not impossible. In our discussion section we contextualise these results, and following this we derive some recommendations for relevant stakeholders in RI teaching.

暂无摘要。

BACKGROUND: The International Committee of Medical Journal Editors (ICMJE) has published Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals. These provide a global standard for writing and editing medical articles, including research integrity. However, no study has examined the research integrity-related content of Japanese medical journals\' Instructions for Authors. We therefore compared research integrity content in ICMJE member journals with those in the English- and Japanese-language journals of the Japanese Association of Medical Sciences (JAMS).
METHODS: This was a descriptive literature study. We obtained Instructions for Authors from English- and Japanese-language journals listed on the JAMS website and the ICMJE member journals listed on the ICMJE website as of September 1, 2021. We compared the presence of 20 topics (19 in the ICMJE Recommendations plus compliance with ICMJE) in the Instructions for Authors, and analyzed the content of the conflict of interest disclosure.
RESULTS: We evaluated 12 ICMJE member journals, and 82 English-language and 99 Japanese-language subcommittee journals. The median number of topics covered was 10.5 for ICMJE member journals, 10 for English-language journals, and three for Japanese-language journals. Compliance with ICMJE was mentioned by 10 (83%) ICMJE member journals, 75 (91%) English-language journals, and 29 (29%) Japanese-language journals. The ICMJE Conflicts of Interest Disclosure Form was requested by seven (64%) ICMJE member journals, 15 (18%) English-language journals, and one (1%) Japanese-language journal.
CONCLUSIONS: Although the topics in the JAMS English-language journals resembled those in the ICMJE member journals, the median value of ICMJE-related topic inclusion was approximately one-third lower in JAMS Japanese-language journals than in ICMJE member journals. It is hoped that Japanese-language journals whose conflict of interest disclosure policies differ from ICMJE standards will adopt international standards to deter misconduct and ensure publication quality.

The application of artificial intelligence (AI) technologies in scientific research has significantly enhanced efficiency and accuracy but also introduced new forms of academic misconduct, such as data fabrication and text plagiarism using AI algorithms. These practices jeopardize research integrity and can mislead scientific directions. This study addresses these challenges, underscoring the need for the academic community to strengthen ethical norms, enhance researcher qualifications, and establish rigorous review mechanisms. To ensure responsible and transparent research processes, we recommend the following specific key actions: Development and enforcement of comprehensive AI research integrity guidelines that include clear protocols for AI use in data analysis and publication, ensuring transparency and accountability in AI-assisted research. Implementation of mandatory AI ethics and integrity training for researchers, aimed at fostering an in-depth understanding of potential AI misuses and promoting ethical research practices. Establishment of international collaboration frameworks to facilitate the exchange of best practices and development of unified ethical standards for AI in research. Protecting research integrity is paramount for maintaining public trust in science, making these recommendations urgent for the scientific community consideration and action.